Computational Phenotyping in Psychiatry: A Worked Example
Computational Phenotyping in Psychiatry: A Worked Example
复制标题
精神病学中的计算表型分析：一个实例
作者：
P. Schwartenbeck;Karl J. Friston
计算精神病学是一个快速发展的领域，它使用基于模型的数量来推断精神病理学背后的行为和神经元异常。如果成功，这种方法有望深入了解（病理）脑功能，并为精神病学提供更机械和定量的方法——构建治疗干预措施，预测反应和复发。计算精神病学的基本程序是建立一个计算模型，将行为或神经元过程形式化。测量的行为（或神经元）反应然后被用来推断单个或一组受试者的模型参数。在这里，我们对这个过程提供了一个说明性的概述，从特定任务中的选择行为建模开始，模拟数据，然后反转该模型以估计群体效应。最后，我们举例说明交叉验证，以评估受试者之间的变量（例如，诊断）是否可以成功恢复。我们的工作示例使用一个简单的两步迷宫任务和一个基于（主动）推理和马尔可夫决策过程的选择行为模型。我们所说明的程序步骤和例程并不局限于特定的研究领域或特定的计算模型，原则上可以应用于计算精神病学的许多领域。
Abstract Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology—structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process. Measured behavioral (or neuronal) responses are then used to infer the model parameters of a single subject or a group of subjects. Here, we provide an illustrative overview over this process, starting from the modeling of choice behavior in a specific task, simulating data, and then inverting that model to estimate group effects. Finally, we illustrate cross-validation to assess whether between-subject variables (e.g., diagnosis) can be recovered successfully. Our worked example uses a simple two-step maze task and a model of choice behavior based on (active) inference and Markov decision processes. The procedural steps and routines we illustrate are not restricted to a specific field of research or particular computational model but can, in principle, be applied in many domains of computational psychiatry.